Service

Development of workflows for data analysis

Breakthroughs in recording technologies and analysis approaches have led to unprecedented levels of complexity in electrophysiological experiments. Most notably, the ability to perform massively parallel recordings from hundreds of neurons in the brain, paired with a strong interest in complex, natural stimulation and behavioral paradigms, leads to a surge of intricately interwoven data sources. These should be analyzed with sophisticated methods to uncover the network dynamics by exploiting the parallel aspect of the data. The combination of these factors leads to highly complex projects that pose a challenge for researchers who, over the course of years, are confronted with planning of the analysis, organization of workflows in larger teams, programming of software, and bookkeeping of the results obtained by constantly evolving analysis methods. The complexity has reached a level where a professionalization of the data analysis workflow, both conceptually and in terms of supporting software infrastructures, has become a necessity. Indeed, in a recent survey among researchers performing data analysis conducted in collaboration with researchers at the CNRS (Paris), a vast majority (93%) of responders stated they believe that improved workflows for electrophysiology are useful for the community.

Workflow schema

Projects conducted at INM-6 typically involve heavy data analysis, and it is therefore essential to rely on efficient and sustainable workflows. We investigate in detail strategies and guidelines that allow us to achieve reproducibility and validation in a highly collaborative environment. To this end, we gather tools that are being developed by the Neuroinformatics community and assemble them into concrete workflows used in our daily work. Where functionality is missing, we extend existing tools, or build new ones. Recent work includes topics such as:

Advancing tools for managing meta data via odML by building use-cases (in collaboration with the German Neuroinformatics Node, http://www.gnode.org)

File I/O development for standardized access to data using neo (http://neuralensemble.org/neo/)

Development of software tools for the analysis of massively parallel functional data